


import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


A   =			logging.getLogger(__name__)





def __UpperCAmelCase							(		__A=2			,    __A=3			,    __A=1_6			,    __A = 1_0			,    __A = 2      )    ->       Optional[int]:



       '''simple docstring'''





       def get_dataset(__A      ):
              UpperCAmelCase__					    =  torch.randn(batch_size * n_batches			,    1      )
              return TensorDataset(a__			,    a * x + b + 0.1 * torch.randn(batch_size * n_batches			,    1      )      )

       UpperCAmelCase__					    =  get_dataset(a__      )
       UpperCAmelCase__					    =  get_dataset(a__      )
       UpperCAmelCase__					    =  DataLoader(a__			,    shuffle=a__			,    batch_size=a__			,    num_workers=4      )
       UpperCAmelCase__					    =  DataLoader(a__			,    shuffle=a__			,    batch_size=a__			,    num_workers=4      )
       return (train_dataloader, valid_dataloader)





def __UpperCAmelCase							(		__A			,    __A			,    __A			,    __A			,    __A			,    __A=None      )    ->       Dict:



       '''simple docstring'''





       UpperCAmelCase__					    =  []
       for epoch in range(a__      ):
              # Train quickly
              model.train()
              for batch in dataloader:
                     UpperCAmelCase__    ,      UpperCAmelCase__					    =  batch
                     UpperCAmelCase__					    =  model(a__      )
                     UpperCAmelCase__					    =  torch.nn.functional.mse_loss(a__			,    a__      )
                     accelerator.backward(a__      )
                     optimizer.step()
                     optimizer.zero_grad()
              rands.append(random.random()      )  # Introduce some randomness
              if scheduler is not None:
                     scheduler.step()
       return rands


class      lowercase__	(       nn.Module		):






  def __init__(       self   :       Dict			):




         """simple docstring"""







         super().__init__()
         UpperCAmelCase__					    =  nn.Parameter(torch.randn(1			)			)
         UpperCAmelCase__					    =  nn.Parameter(torch.randn(1			)			)






  def       _UpperCAmelCase	(       self   :       List[str]		,    _lowercase   :       int			):




         """simple docstring"""







         return x * self.a + self.b




class      lowercase__	(       unittest.TestCase		):






  def       _UpperCAmelCase	(       self   :       Any			):




         """simple docstring"""







         with tempfile.TemporaryDirectory() as tmpdir:
                set_seed(42			)
                UpperCAmelCase__					    =  DummyModel()
                UpperCAmelCase__					    =  torch.optim.Adam(params=model.parameters()		,    lr=1E-3			)
                UpperCAmelCase__    ,      UpperCAmelCase__					    =  dummy_dataloaders()
                UpperCAmelCase__					    =  ProjectConfiguration(total_limit=1		,    project_dir=lowercase_		,    automatic_checkpoint_naming=lowercase_			)
                # Train baseline
                UpperCAmelCase__					    =  Accelerator(project_config=lowercase_			)
                UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__					    =  accelerator.prepare(
                    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                # Save initial
                accelerator.save_state()

                # Save second state
                accelerator.save_state()
                self.assertEqual(len(os.listdir(accelerator.project_dir			)			)		,    1			)






  def       _UpperCAmelCase	(       self   :       int			):




         """simple docstring"""







         with tempfile.TemporaryDirectory() as tmpdir:
                set_seed(42			)
                UpperCAmelCase__					    =  DummyModel()
                UpperCAmelCase__					    =  torch.optim.Adam(params=model.parameters()		,    lr=1E-3			)
                UpperCAmelCase__    ,      UpperCAmelCase__					    =  dummy_dataloaders()
                # Train baseline
                UpperCAmelCase__					    =  Accelerator()
                UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__					    =  accelerator.prepare(
                    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                # Save initial
                UpperCAmelCase__					    =  os.path.join(lowercase_		,    "initial"			)
                accelerator.save_state(lowercase_			)
                ((UpperCAmelCase__)    ,      (UpperCAmelCase__))					    =  model.a.item(), model.b.item()
                UpperCAmelCase__					    =  optimizer.state_dict()
                UpperCAmelCase__					    =  train(3		,    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                ((UpperCAmelCase__)    ,      (UpperCAmelCase__))					    =  model.a.item(), model.b.item()
                UpperCAmelCase__					    =  optimizer.state_dict()

                # Train partially
                set_seed(42			)
                UpperCAmelCase__					    =  DummyModel()
                UpperCAmelCase__					    =  torch.optim.Adam(params=model.parameters()		,    lr=1E-3			)
                UpperCAmelCase__    ,      UpperCAmelCase__					    =  dummy_dataloaders()
                UpperCAmelCase__					    =  Accelerator()
                UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__					    =  accelerator.prepare(
                    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                accelerator.load_state(lowercase_			)
                ((UpperCAmelCase__)    ,      (UpperCAmelCase__))					    =  model.a.item(), model.b.item()
                UpperCAmelCase__					    =  optimizer.state_dict()
                self.assertEqual(lowercase_		,    lowercase_			)
                self.assertEqual(lowercase_		,    lowercase_			)
                self.assertEqual(lowercase_		,    lowercase_			)

                UpperCAmelCase__					    =  train(2		,    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                # Save everything
                UpperCAmelCase__					    =  os.path.join(lowercase_		,    "checkpoint"			)
                accelerator.save_state(lowercase_			)

                # Load everything back in and make sure all states work
                accelerator.load_state(lowercase_			)
                test_rands += train(1		,    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                ((UpperCAmelCase__)    ,      (UpperCAmelCase__))					    =  model.a.item(), model.b.item()
                UpperCAmelCase__					    =  optimizer.state_dict()
                self.assertEqual(lowercase_		,    lowercase_			)
                self.assertEqual(lowercase_		,    lowercase_			)
                self.assertEqual(lowercase_		,    lowercase_			)
                self.assertEqual(lowercase_		,    lowercase_			)






  def       _UpperCAmelCase	(       self   :       List[str]			):




         """simple docstring"""







         with tempfile.TemporaryDirectory() as tmpdir:
                set_seed(42			)
                UpperCAmelCase__					    =  DummyModel()
                UpperCAmelCase__					    =  torch.optim.Adam(params=model.parameters()		,    lr=1E-3			)
                UpperCAmelCase__    ,      UpperCAmelCase__					    =  dummy_dataloaders()
                UpperCAmelCase__					    =  ProjectConfiguration(automatic_checkpoint_naming=lowercase_			)

                # Train baseline
                UpperCAmelCase__					    =  Accelerator(project_dir=lowercase_		,    project_config=lowercase_			)
                UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__					    =  accelerator.prepare(
                    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                # Save initial
                accelerator.save_state()
                ((UpperCAmelCase__)    ,      (UpperCAmelCase__))					    =  model.a.item(), model.b.item()
                UpperCAmelCase__					    =  optimizer.state_dict()
                UpperCAmelCase__					    =  train(3		,    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                ((UpperCAmelCase__)    ,      (UpperCAmelCase__))					    =  model.a.item(), model.b.item()
                UpperCAmelCase__					    =  optimizer.state_dict()

                # Train partially
                set_seed(42			)
                UpperCAmelCase__					    =  DummyModel()
                UpperCAmelCase__					    =  torch.optim.Adam(params=model.parameters()		,    lr=1E-3			)
                UpperCAmelCase__    ,      UpperCAmelCase__					    =  dummy_dataloaders()
                UpperCAmelCase__					    =  ProjectConfiguration(iteration=1		,    automatic_checkpoint_naming=lowercase_			)
                UpperCAmelCase__					    =  Accelerator(project_dir=lowercase_		,    project_config=lowercase_			)
                UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__					    =  accelerator.prepare(
                    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                accelerator.load_state(os.path.join(lowercase_		,    "checkpoints"		,    "checkpoint_0"			)			)
                ((UpperCAmelCase__)    ,      (UpperCAmelCase__))					    =  model.a.item(), model.b.item()
                UpperCAmelCase__					    =  optimizer.state_dict()
                self.assertEqual(lowercase_		,    lowercase_			)
                self.assertEqual(lowercase_		,    lowercase_			)
                self.assertEqual(lowercase_		,    lowercase_			)

                UpperCAmelCase__					    =  train(2		,    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                # Save everything
                accelerator.save_state()

                # Load everything back in and make sure all states work
                accelerator.load_state(os.path.join(lowercase_		,    "checkpoints"		,    "checkpoint_1"			)			)
                test_rands += train(1		,    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                ((UpperCAmelCase__)    ,      (UpperCAmelCase__))					    =  model.a.item(), model.b.item()
                UpperCAmelCase__					    =  optimizer.state_dict()
                self.assertEqual(lowercase_		,    lowercase_			)
                self.assertEqual(lowercase_		,    lowercase_			)
                self.assertEqual(lowercase_		,    lowercase_			)
                self.assertEqual(lowercase_		,    lowercase_			)






  def       _UpperCAmelCase	(       self   :       str			):




         """simple docstring"""







         UpperCAmelCase__					    =  torch.tensor([1, 2, 3]			)
         UpperCAmelCase__					    =  torch.tensor([2, 3, 4]			)
         UpperCAmelCase__					    =  DummyModel()
         UpperCAmelCase__					    =  torch.optim.Adam(net.parameters()			)
         UpperCAmelCase__					    =  Accelerator()
         with self.assertRaises(lowercase_			) as ve:
                accelerator.register_for_checkpointing(lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
         UpperCAmelCase__					    =  str(ve.exception			)
         self.assertTrue("Item at index 0" in message			)
         self.assertTrue("Item at index 1" in message			)
         self.assertFalse("Item at index 2" in message			)
         self.assertFalse("Item at index 3" in message			)






  def       _UpperCAmelCase	(       self   :       int			):




         """simple docstring"""







         with tempfile.TemporaryDirectory() as tmpdir:
                set_seed(42			)
                UpperCAmelCase__					    =  DummyModel()
                UpperCAmelCase__					    =  torch.optim.Adam(params=model.parameters()		,    lr=1E-3			)
                UpperCAmelCase__					    =  torch.optim.lr_scheduler.StepLR(lowercase_		,    step_size=1		,    gamma=0.9_9			)
                UpperCAmelCase__    ,      UpperCAmelCase__					    =  dummy_dataloaders()
                UpperCAmelCase__					    =  ProjectConfiguration(automatic_checkpoint_naming=lowercase_			)
                # Train baseline
                UpperCAmelCase__					    =  Accelerator(project_dir=lowercase_		,    project_config=lowercase_			)
                UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__					    =  accelerator.prepare(
                    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                # Save initial
                accelerator.save_state()
                UpperCAmelCase__					    =  scheduler.state_dict()
                train(3		,    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_			)
                self.assertNotEqual(lowercase_		,    scheduler.state_dict()			)

                # Load everything back in and make sure all states work
                accelerator.load_state(os.path.join(lowercase_		,    "checkpoints"		,    "checkpoint_0"			)			)
                self.assertEqual(lowercase_		,    scheduler.state_dict()			)






  def       _UpperCAmelCase	(       self   :       int			):




         """simple docstring"""







         with tempfile.TemporaryDirectory() as tmpdir:
                set_seed(42			)
                UpperCAmelCase__					    =  DummyModel()
                UpperCAmelCase__					    =  ProjectConfiguration(automatic_checkpoint_naming=lowercase_		,    total_limit=2			)
                # Train baseline
                UpperCAmelCase__					    =  Accelerator(project_dir=lowercase_		,    project_config=lowercase_			)
                UpperCAmelCase__					    =  accelerator.prepare(lowercase_			)
                # Save 3 states:
                for _ in range(11			):
                       accelerator.save_state()
                self.assertTrue(not os.path.exists(os.path.join(lowercase_		,    "checkpoints"		,    "checkpoint_0"			)			)			)
                self.assertTrue(os.path.exists(os.path.join(lowercase_		,    "checkpoints"		,    "checkpoint_9"			)			)			)
                self.assertTrue(os.path.exists(os.path.join(lowercase_		,    "checkpoints"		,    "checkpoint_10"			)			)			)






  @require_cuda
  def       _UpperCAmelCase	(       self   :       Union[str, Any]			):




         """simple docstring"""







         UpperCAmelCase__					    =  ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__			)]
         execute_subprocess_async(lowercase_		,    env=os.environ.copy()			)


if __name__ == "__main__":
    A   =			"/tmp/accelerate/state_checkpointing"
    A   =			DummyModel()
    A   =			torch.optim.Adam(params=model.parameters(), lr=1e-3)
    A   =			torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
    A,			A   =			dummy_dataloaders()
    A   =			ProjectConfiguration(automatic_checkpoint_naming=True)
    # Train baseline
    A   =			Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="no")
    if accelerator.process_index == 0:
        if os.path.exists(savedir):
            shutil.rmtree(savedir)
        os.makedirs(savedir)
    A,			A,			A,			A,			A   =			accelerator.prepare(
        model, optimizer, train_dataloader, valid_dataloader, scheduler
    )
    A,			A   =			accelerator.prepare(model, optimizer)
    train(3, model, train_dataloader, optimizer, accelerator, scheduler)
    # Check that the intial optimizer is loaded on the GPU
    for group in optimizer.param_groups:
        A   =			group["params"][0].device
        break
    assert param_device.type == accelerator.device.type
    A   =			model.cpu()
    accelerator.wait_for_everyone()
    accelerator.save_state()
    accelerator.wait_for_everyone()

    # Check CPU state
    accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="cpu")
    for group in optimizer.param_groups:
        A   =			group["params"][0].device
        break
    assert (
        param_device.type == torch.device("cpu").type
    ), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

    # Check device state
    model.to(accelerator.device)
    accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="on_device")
    for group in optimizer.param_groups:
        A   =			group["params"][0].device
        break
    assert (
        param_device.type == accelerator.device.type
    ), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

    # Check error
    with pytest.raises(TypeError, match="Unsupported optimizer map location passed"):
        accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="invalid")
    accelerator.wait_for_everyone()
    if accelerator.process_index == 0:
        shutil.rmtree(savedir)
    accelerator.wait_for_everyone()






import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator


def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  _UpperCamelCase           =    fname.split(os.path.sep )[-1]
  return re.search(r"^(.*)_\d+\.jpg$"    ,     a__ ).groups()[0]




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   int							, lowercase_				:   Dict=None							, lowercase_				:   Optional[Any]=None)						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    file_names
   _UpperCamelCase           =    image_transform
   _UpperCamelCase           =    label_to_id







 def __len__(		self				:   int)						->	Optional[int]:


   """simple docstring"""







   return len(self.file_names)







 def __getitem__(		self				:   int							, lowercase_				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    self.file_names[idx]
   _UpperCamelCase           =    PIL.Image.open(lowercase_)
   _UpperCamelCase           =    raw_image.convert("RGB")
   if self.image_transform is not None:
     _UpperCamelCase           =    self.image_transform(lowercase_)
   _UpperCamelCase           =    extract_label(lowercase_)
   if self.label_to_id is not None:
     _UpperCamelCase           =    self.label_to_id[label]
   return {"image": image, "label": label}


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[Any]:




  '''simple docstring'''

  if args.with_tracking:
    _UpperCamelCase           =    Accelerator(
        cpu=args.cpu    ,     mixed_precision=args.mixed_precision    ,     log_with="all"    ,     project_dir=args.project_dir )
  else:
    _UpperCamelCase           =    Accelerator(cpu=args.cpu    ,     mixed_precision=args.mixed_precision )

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCamelCase           =    config["lr"]
  _UpperCamelCase           =    int(config["num_epochs"] )
  _UpperCamelCase           =    int(config["seed"] )
  _UpperCamelCase           =    int(config["batch_size"] )
  _UpperCamelCase           =    config["image_size"]
  if not isinstance(a__    ,     (list, tuple) ):
    _UpperCamelCase           =    (image_size, image_size)

  # Parse out whether we are saving every epoch or after a certain number of batches
  if hasattr(args.checkpointing_steps    ,     "isdigit" ):
    if args.checkpointing_steps == "epoch":
      _UpperCamelCase           =    args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _UpperCamelCase           =    int(args.checkpointing_steps )
    else:
      raise ValueError(
          f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.' )
  else:
    _UpperCamelCase           =    None

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    _UpperCamelCase           =    os.path.split(a__ )[-1].split("." )[0]
    accelerator.init_trackers(a__    ,     a__ )

  # Grab all the image filenames
  _UpperCamelCase           =    [os.path.join(args.data_dir    ,     a__ ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]

  # Build the label correspondences
  _UpperCamelCase           =    [extract_label(a__ ) for fname in file_names]
  _UpperCamelCase           =    list(set(a__ ) )
  id_to_label.sort()
  _UpperCamelCase           =    {lbl: i for i, lbl in enumerate(a__ )}

  # Set the seed before splitting the data.
  np.random.seed(a__ )
  torch.manual_seed(a__ )
  torch.cuda.manual_seed_all(a__ )

  # Split our filenames between train and validation
  _UpperCamelCase           =    np.random.permutation(len(a__ ) )
  _UpperCamelCase           =    int(0.8 * len(a__ ) )
  _UpperCamelCase           =    random_perm[:cut]
  _UpperCamelCase           =    random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  _UpperCamelCase           =    Compose([RandomResizedCrop(a__    ,     scale=(0.5, 1.0) ), ToTensor()] )
  _UpperCamelCase           =    PetsDataset(
      [file_names[i] for i in train_split]    ,     image_transform=a__    ,     label_to_id=a__ )

  # For evaluation, we use a deterministic Resize
  _UpperCamelCase           =    Compose([Resize(a__ ), ToTensor()] )
  _UpperCamelCase           =    PetsDataset([file_names[i] for i in eval_split]    ,     image_transform=a__    ,     label_to_id=a__ )

  # Instantiate dataloaders.
  _UpperCamelCase           =    DataLoader(a__    ,     shuffle=a__    ,     batch_size=a__    ,     num_workers=4 )
  _UpperCamelCase           =    DataLoader(a__    ,     shuffle=a__    ,     batch_size=a__    ,     num_workers=4 )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _UpperCamelCase           =    create_model("resnet50d"    ,     pretrained=a__    ,     num_classes=len(a__ ) )

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  _UpperCamelCase           =    model.to(accelerator.device )

  # Freezing the base model
  for param in model.parameters():
    _UpperCamelCase           =    False
  for param in model.get_classifier().parameters():
    _UpperCamelCase           =    True

  # We normalize the batches of images to be a bit faster.
  _UpperCamelCase           =    torch.tensor(model.default_cfg["mean"] )[None, :, None, None].to(accelerator.device )
  _UpperCamelCase           =    torch.tensor(model.default_cfg["std"] )[None, :, None, None].to(accelerator.device )

  # Instantiate optimizer
  _UpperCamelCase           =    torch.optim.Adam(params=model.parameters()    ,     lr=lr / 25 )

  # Instantiate learning rate scheduler
  _UpperCamelCase           =    OneCycleLR(optimizer=a__    ,     max_lr=a__    ,     epochs=a__    ,     steps_per_epoch=len(a__ ) )

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    accelerator.prepare(
      a__    ,     a__    ,     a__    ,     a__    ,     a__ )
  # We need to keep track of how many total steps we have iterated over
  _UpperCamelCase           =    0
  # We also need to keep track of the starting epoch so files are named properly
  _UpperCamelCase           =    0

  # Potentially load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}' )
      accelerator.load_state(args.resume_from_checkpoint )
      _UpperCamelCase           =    os.path.basename(args.resume_from_checkpoint )
    else:
      # Get the most recent checkpoint
      _UpperCamelCase           =    [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
      dirs.sort(key=os.path.getctime )
      _UpperCamelCase           =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _UpperCamelCase           =    os.path.splitext(a__ )[0]

    if "epoch" in training_difference:
      _UpperCamelCase           =    int(training_difference.replace("epoch_"    ,     "" ) ) + 1
      _UpperCamelCase           =    None
    else:
      _UpperCamelCase           =    int(training_difference.replace("step_"    ,     "" ) )
      _UpperCamelCase           =    resume_step // len(a__ )
      resume_step -= starting_epoch * len(a__ )

    # Now we train the model
  for epoch in range(a__    ,     a__ ):
    model.train()
    if args.with_tracking:
      _UpperCamelCase           =    0
    if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
      # We need to skip steps until we reach the resumed step
      _UpperCamelCase           =    accelerator.skip_first_batches(a__    ,     a__ )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _UpperCamelCase           =    train_dataloader
    for batch in active_dataloader:
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _UpperCamelCase           =    {k: v.to(accelerator.device ) for k, v in batch.items()}
      _UpperCamelCase           =    (batch["image"] - mean) / std
      _UpperCamelCase           =    model(a__ )
      _UpperCamelCase           =    torch.nn.functional.cross_entropy(a__    ,     batch["label"] )
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(a__ )
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      overall_step += 1
      if isinstance(a__    ,     a__ ):
        _UpperCamelCase           =    f'step_{overall_step}'
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _UpperCamelCase           =    os.path.join(args.output_dir    ,     a__ )
          accelerator.save_state(a__ )
    model.eval()
    _UpperCamelCase           =    0
    _UpperCamelCase           =    0
    for step, batch in enumerate(a__ ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _UpperCamelCase           =    {k: v.to(accelerator.device ) for k, v in batch.items()}
      _UpperCamelCase           =    (batch["image"] - mean) / std
      with torch.no_grad():
        _UpperCamelCase           =    model(a__ )
      _UpperCamelCase           =    outputs.argmax(dim=-1 )
      _UpperCamelCase      ,						_UpperCamelCase           =    accelerator.gather_for_metrics((predictions, batch["label"]) )
      _UpperCamelCase           =    predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    _UpperCamelCase           =    accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}: {100 * eval_metric:.2f}' )
    if args.with_tracking:
      accelerator.log(
          {
              "accuracy": 100 * eval_metric,
              "train_loss": total_loss.item() / len(a__ ),
              "epoch": epoch,
          }    ,     step=a__    ,     )
    if checkpointing_steps == "epoch":
      _UpperCamelCase           =    f'epoch_{epoch}'
      if args.output_dir is not None:
        _UpperCamelCase           =    os.path.join(args.output_dir    ,     a__ )
      accelerator.save_state(a__ )

  if args.with_tracking:
    accelerator.end_training()


def 				lowerCAmelCase__       (			)      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    argparse.ArgumentParser(description="Simple example of training script." )
  parser.add_argument("--data_dir"    ,     required=a__    ,     help="The data folder on disk." )
  parser.add_argument("--fp16"    ,     action="store_true"    ,     help="If passed, will use FP16 training." )
  parser.add_argument(
      "--mixed_precision"    ,     type=a__    ,     default=a__    ,     choices=["no", "fp16", "bf16", "fp8"]    ,     help="Whether to use mixed precision. Choose"
      "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
      "and an Nvidia Ampere GPU."    ,     )
  parser.add_argument("--cpu"    ,     action="store_true"    ,     help="If passed, will train on the CPU." )
  parser.add_argument(
      "--checkpointing_steps"    ,     type=a__    ,     default=a__    ,     help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."    ,     )
  parser.add_argument(
      "--output_dir"    ,     type=a__    ,     default="."    ,     help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,     )
  parser.add_argument(
      "--resume_from_checkpoint"    ,     type=a__    ,     default=a__    ,     help="If the training should continue from a checkpoint folder."    ,     )
  parser.add_argument(
      "--with_tracking"    ,     action="store_true"    ,     help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,     )
  parser.add_argument(
      "--project_dir"    ,     type=a__    ,     default="logs"    ,     help="Location on where to store experiment tracking logs` and relevent project information"    ,     )
  _UpperCamelCase           =    parser.parse_args()
  _UpperCamelCase           =    {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
  training_function(a__    ,     a__ )


if __name__ == "__main__":
   main()




